End-to-End Quantum-like Language Models with Application to Question Answering

Language Modeling (LM) is a fundamental research topic ina range of areas. Recently, inspired by quantum theory, a novel Quantum Language Model (QLM) has been proposed for Information Retrieval (IR). In this paper, we aim to broaden the theoretical and practical basis of QLM. We develop a Neural Network based Quantum-like Language Model (NNQLM) and apply it to Question Answering. Specifically, based on word embeddings, we design a new density matrix, which represents a sentence (e.g., a question or an answer) and encodes a mixture of semantic subspaces. Such a density matrix, together with a joint representation of the question and the answer, can be integrated into neural network architectures (e.g., 2-dimensional convolutional neural networks). Experiments on the TREC-QA and WIKIQA datasets have verified the effectiveness of our proposed models

Comprehensive molecular characterization of gastric adenocarcinoma

Gastric cancer is a leading cause of cancer deaths, but analysis of its molecular and clinical characteristics has been complicated by histological and aetiological heterogeneity. Here we describe a comprehensive molecular evaluation of 295 primary gastric adenocarcinomas as part of The Cancer Genome Atlas (TCGA) project. We propose a molecular classification dividing gastric cancer into four subtypes: tumours positive for Epstein–Barr virus, which display recurrent PIK3CA mutations, extreme DNA hypermethylation, and amplification of JAK2, CD274 (also known as PD-L1) and PDCD1LG2 (also knownasPD-L2); microsatellite unstable tumours, which show elevated mutation rates, including mutations of genes encoding targetable oncogenic signalling proteins; genomically stable tumours, which are enriched for the diffuse histological variant and mutations of RHOA or fusions involving RHO-family GTPase-activating proteins; and tumours with chromosomal instability, which show marked aneuploidy and focal amplification of receptor tyrosine kinases. Identification of these subtypes provides a roadmap for patient stratification and trials of targeted therapies

Multiplatform Analysis of 12 Cancer Types Reveals Molecular Classification within and across Tissues of Origin

Recent genomic analyses of pathologically-defined tumor types identify “within-a-tissue” disease subtypes. However, the extent to which genomic signatures are shared across tissues is still unclear. We performed an integrative analysis using five genome-wide platforms and one proteomic platform on 3,527 specimens from 12 cancer types, revealing a unified classification into 11 major subtypes. Five subtypes were nearly identical to their tissue-of-origin counterparts, but several distinct cancer types were found to converge into common subtypes. Lung squamous, head & neck, and a subset of bladder cancers coalesced into one subtype typified by TP53 alterations, TP63 amplifications, and high expression of immune and proliferation pathway genes. Of note, bladder cancers split into three pan-cancer subtypes. The multi-platform classification, while correlated with tissue-of-origin, provides independent information for predicting clinical outcomes. All datasets are available for data-mining from a unified resource to support further biological discoveries and insights into novel therapeutic strategies

RockSeg: A Novel Semantic Segmentation Network Based on a Hybrid Framework Combining a Convolutional Neural Network and Transformer for Deep Space Rock Images

Rock detection on the surface of celestial bodies is critical in the deep space environment for obstacle avoidance and path planning of space probes. However, in the remote and complex deep environment, rocks have the characteristics of irregular shape, being similar to the background, sparse pixel characteristics, and being easy for light and dust to affect. Most existing methods face significant challenges to attain high accuracy and low computational complexity in rock detection. In this paper, we propose a novel semantic segmentation network based on a hybrid framework combining CNN and transformer for deep space rock images, namely RockSeg. The network includes a multiscale low-level feature fusion (MSF) module and an efficient backbone network for feature extraction to achieve the effective segmentation of the rocks. Firstly, in the network encoder, we propose a new backbone network (Resnet-T) that combines the part of the Resnet backbone and the transformer block with a multi-headed attention mechanism to capture the global context information. Additionally, a simple and efficient multiscale feature fusion module is designed to fuse low-level features at different scales to generate richer and more detailed feature maps. In the network decoder, these feature maps are integrated with the output feature maps to obtain more precise semantic segmentation results. Finally, we conduct experiments on two deep space rock datasets: the MoonData and MarsData datasets. The experimental results demonstrate that the proposed model outperforms state-of-the-art rock detection algorithms under the conditions of low computational complexity and fast inference speed

Differences of methods to quantify construction and demolition waste for less-developed but fast-growing countries: China as a case study

As China and other developing countries continue to urbanize over the next decades, construction and demolition waste (CDW) management has been becoming a significant challenge for urban sustainability in terms of the environment, economy, and safety. However, accurate estimations or statistics of CDW generation are absent from the official national report in spite of their importance to devise sensible interventions to tackle CDW-related problems. This paper examines and compares the applications of three prevailing methods for estimating CDW, including the weight-per-construction-area method (WAM), buildings’ life span-based method, and weight-per-capita method. Specifically, China has been chosen as the case study. This study implies that the weight-per-construction-area method is more appropriate because of the data availability and accuracy at a city or national level. The results of WAM indicate that a total of 4.1 billion metric tons (Bt) of CDW were generated in China in 2016, mainly from demolition waste (85%). Taking the changes of buildings’ life span into account, a projection analysis reveals that the cumulative CDW generation will be 50 Bt between 2017 and 2040 in China (equal to approximately 38 years cumulative generation of global municipal solid waste). Overall, the findings provide some methodological options for scholars, practitioners, and decision-makers to more accurately estimate the amount of the CDW and to develop a more environmentally sound management strategy.status: publishe

Defect engineering of low-coordinated metal-organic frameworks (MOFs) for improved CO₂ access and capture

While metal-organic frameworks (MOFs) are promising gas adsorbents, their tortuous microporous structures cause additional resistance for gas diffusion, thus hindering the accessibility of interior active sites. Here, we present a practical strategy to incorporate missing cluster defects into a representative low-coordinated MOFs structure, Mg-MOF-74, while maintaining the stability of a defect-rich structure. In this proposed method, graphene oxide (GO) is employed as modulator, and crystallization time is varied to promote defect formation by altering the nucleation and crystal growth processes. The best performing GO-modified Mg-MOF-74 sample (MOF@GO 40 h) achieved 18% and 15% improvement in surface area and total pore volume, respectively, over pristine Mg-MOF-74. The reduced diffusion resistance to gas flow translates to increased accessibility for gas molecules to active Mg adsorption sites inside the MOFs, leading to enhanced CO2 capture performance; the CO2 uptake quantity of MOF@GO 40 h arrives at 6.06 mmol/g at 0.1 bar and at 9.17 mmol/g at 1 bar and 25 °C, 19.29% and 16.37% higher, respectively, than that of the pristine Mg-MOF-74, with a CO2/N2 selectivity around 17.36% greater than that of pristine Mg-MOF-74. Our study demonstrates a facile approach for incorporating defects into MOFs systems with low coordination environments, thus expanding the library of defect-rich MOFs beyond the current highly coordinated MOF systems.This work is supported by SUTD GAP Funding

Water Uptake Patterns of Alfalfa under Winter Irrigation in Cold and Arid Grassland

Crop reduction caused by cryogenesis and drought is a serious and global problem. The environmental stress caused by low temperature and drought during the overwintering stage of forage is the key factor leading to this low yield. In cold and arid grassland, winter irrigation can effectively alleviate the stress of alfalfa during overwintering, improve the survival rate of alfalfa, and significantly increase the yield. However, the water uptake patterns of alfalfa under winter irrigation are not clear, which are important to explore the mechanism of alleviating environmental stress by winter irrigation. In this research, the stable isotope compositions of all probable water sources and alfalfa xylem water were measured after winter irrigation. A graphical method was applied to identify the main soil layers with water uptake by the alfalfa roots. The contribution rate of available water sources to alfalfa xylem water was quantified by the MixSIAR (Bayesian isotope analysis mixing model in R) model. The results indicated that alfalfa absorbed soil water when the soil water content was high enough in the root layer when under high water volume freezing irrigation (irrigation in early winter when soil is freezing) but not under low and medium water volume freezing irrigation. Alfalfa gradually began to absorb soil water on the third day after thawing irrigation (irrigation in late winter when the soil is thawing) and showed different water uptake characteristics under low, medium, and high water volume. Thawing irrigation also accelerated the regeneration of alfalfa

Preparation and Properties of Cyanobacteria-Based Carbon Quantum Dots/Polyvinyl Alcohol/ Nanocellulose Composite

Blue luminescent carbon quantum dots (CQDs) were prepared from cyanobacteria by a hydrothermal method. The PL quantum yields of the obtained CQDs was 5.30%. Cyanobacteria-based carbon quantum dots/polyvinyl alcohol/nanocellulose composite films were prepared, which could emit bright blue under UV light. FTIR characterization showed that the composite films had hydroxyl groups on the surface and no new groups were formed after combining the three materials. The photoluminescence (PL) spectra revealed that the emission of the prepared CQDs was excitation dependent. Studies on the water resistance performance and light barrier properties of the composite films showed that they possessed higher water resistance properties and better UV/infrared light barrier properties. Therefore, we report the cyanobacteria-based carbon quantum dots/polyvinyl alcohol/nanocellulose composite films have the potential to be applied in flexible packaging materials, anti-fake materials, UV/infrared light barrier materials and so on

The chemical damage of sandstone after sulfuric acid-rock reactions with different duration times and its influence on the impact mechanical behaviour

The low-permeability characteristic of sandstone-type uranium deposits has become the key geological bottleneck during the in-situ leaching mining, seriously restricting the development and utilization of uranium resources in China. At present, the blasting-enhanced permeability (BEP) and acidizing-enhanced permeability (AEP) are confirmed to be mainstream approaches to enhance the reservoir permeability of low-permeability sandstone-type uranium deposit (LPSUD). To clarify the synergistic effect of BEP and AEP, the acid-rock reaction and dynamic impact experiments were conducted, aiming to study the effect of chemical reactions on pore structure, dynamic mechanical properties and failure pattern of sandstone. Results show that with the increasing acid-rock reaction time, the total pore volume of samples is promoted largely and exhibits obvious chemical damage. The change of pore volume depends on the pore size, the 100–1000 nm and 1000–10000 nm pores are more susceptible to acid-rock reactions. The dynamic peak strength and the dynamic elastic modulus are decreased and the dynamic peak strain and strain rate are increased when lengthening the acid-rock reaction time, whose evolution laws can be fitted by the logistic expression, the linear expression and the exponential expression, respectively. The acid-rock reactions also have an influence on the fracture development of samples after the dynamic impact. The damaged fractures on the end faces of samples grow from the isolated short fracture, the isolated long fracture to the fracture network, and the damaged fractures on the sides of samples develop from the non-penetration fractures, penetration fractures to the multi-branch fractures. This study clarifies the physical and chemical combined damage mechanism, demonstrates the potential of reservoir stimulation by uniting the BEP and the AEP, and provides a theoretical reference for the reservoir stimulation of LPSUD

Pick and place process for uniform shrinking of 3D printed micro- and nano-architected materials

Abstract Two-photon polymerization lithography is promising for producing three-dimensional structures with user-defined micro- and nanoscale features. Additionally, shrinkage by thermolysis can readily shorten the lattice constant of three-dimensional photonic crystals and enhance their resolution and mechanical properties; however, this technique suffers from non-uniform shrinkage owing to substrate pinning during heating. Here, we develop a simple method using poly(vinyl alcohol)-assisted uniform shrinking of three-dimensional printed structures. Microscopic three-dimensional printed objects are picked and placed onto a receiving substrate, followed by heating to induce shrinkage. We show the successful uniform heat-shrinking of three-dimensional prints with various shapes and sizes, without sacrificial support structures, and observe that the surface properties of the receiving substrate are important factors for uniform shrinking. Moreover, we print a three-dimensional mascot model that is then uniformly shrunk, producing vivid colors from colorless woodpile photonic crystals. The proposed method has significant potential for application in mechanics, optics, and photonics